AI-Powered Data Engineering: Automating ETL Pipelines for Scalable Cloud Analytics
Keywords:
AI-powered ETL, data engineering automation, cloud analytics, predictive analyticsAbstract
AI has revolutionised the data engineering in automation of Extract, Transform, Load (ETL) pipelines. This significantly enhances the efficiency, scalability, and reliability of cloud-based analytics. The objective of this research paper is to dive into the application of artificial intelligence in ETL automation workflow, data ingestion, transformation, and governance across modern cloud platforms such as Snowflake, Apache Airflow, and SnapLogic. By utilising machine learning algorithms system can optimise schema evolution, anomaly detection, and data quality management. This results in reducing operational overhead and minimising human intervention in data pipeline.
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